Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,996 Bytes
29d49a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
# Openpose
# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
# 3rd Edited by ControlNet
# 4th Edited by ControlNet (added face and correct hands)
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import cv2
import torch
import numpy as np
from PIL import Image
from ..util import HWC3, resize_image
from . import util
def draw_pose(pose, H, W):
bodies = pose['bodies']
faces = pose['faces']
hands = pose['hands']
candidate = bodies['candidate']
subset = bodies['subset']
canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
canvas = util.draw_bodypose(canvas, candidate, subset)
canvas = util.draw_handpose(canvas, hands)
canvas = util.draw_facepose(canvas, faces)
return canvas
class DWposeDetector:
def __init__(self, det_config=None, det_ckpt=None, pose_config=None, pose_ckpt=None, device="cpu"):
from .wholebody import Wholebody
self.pose_estimation = Wholebody(det_config, det_ckpt, pose_config, pose_ckpt, device)
def to(self, device):
self.pose_estimation.to(device)
return self
def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
input_image = cv2.cvtColor(np.array(input_image, dtype=np.uint8), cv2.COLOR_RGB2BGR)
input_image = HWC3(input_image)
input_image = resize_image(input_image, detect_resolution)
H, W, C = input_image.shape
with torch.no_grad():
candidate, subset = self.pose_estimation(input_image)
nums, keys, locs = candidate.shape
candidate[..., 0] /= float(W)
candidate[..., 1] /= float(H)
body = candidate[:,:18].copy()
body = body.reshape(nums*18, locs)
score = subset[:,:18]
for i in range(len(score)):
for j in range(len(score[i])):
if score[i][j] > 0.3:
score[i][j] = int(18*i+j)
else:
score[i][j] = -1
un_visible = subset<0.3
candidate[un_visible] = -1
foot = candidate[:,18:24]
faces = candidate[:,24:92]
hands = candidate[:,92:113]
hands = np.vstack([hands, candidate[:,113:]])
bodies = dict(candidate=body, subset=score)
pose = dict(bodies=bodies, hands=hands, faces=faces)
detected_map = draw_pose(pose, H, W)
detected_map = HWC3(detected_map)
img = resize_image(input_image, image_resolution)
H, W, C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map
|